Joint Bitrate Adaptation and Feature Extraction for Edge-Assisted Localization of Mobile Devices

The present disclosure relates to Simultaneous Localization and Mapping (SLAM) for a mobile device and, more specifically, server, or edge, assisted SLAM.

Localization and mapping, which is also known as Simultaneous Localization and Mapping (SLAM), refers to a computer-implemented procedure for constructing or updating a map an unknown environment while simultaneously keeping track of a mobile device's location within the unknown environment. Existing SLAM procedures are oftentimes implemented at the mobile device and are very energy demanding. As such, there is a need for systems and methods that reduce the energy demands of a SLAM procedure on the mobile device.

Systems and methods are disclosed for joint bitrate adaptation and feature extraction for server, or edge, assisted localization of mobile devices. In one embodiment, a method performed by a mobile device for server-assisted localization and mapping comprises obtaining an image of an environment in which the mobile device is located from an associated camera and determining a desired bitrate for compression of the image based on one or more network conditions of a wireless network over which the mobile device is to transmit the image to an associated server for server-assisted localization and mapping. The method further comprises determining whether one or more criteria for transmission of the image at the desired bitrate are satisfied, the one or more criteria comprising a criterion that the image is to be transmitted at the desired bitrate if the desired bitrate is greater than a predefined or configured threshold bitrate for server-assisted localization and mapping. The method further comprises, if the one or more criteria are not satisfied, performing a feature extraction procedure that detects one or more features in the image and provides feature information comprising information that describes the one or more features detected in the image and information that indicates locations of the one or more features within the image and transmitting the feature information to the associated server for server-assisted localization and mapping. The method further comprises, if any of the one or more criteria are satisfied, compressing the image at the desired bitrate to provide a compressed image and transmitting the compressed image to the associated server for server-assisted localization and mapping. In this manner, image compression can be performed while also maintaining an acceptable level of performance for the server-assisted localization and mapping.

In one embodiment, the one or more criteria for transmission of the image at the desired bitrate comprises a criterion that the image is to be transmitted at the desired bitrate if the image is a keyframe, even if the desired bitrate is less than the predefined or configured threshold bitrate for server-assisted localization and mapping. In one embodiment, keyframes for the server-assisted localization and mapping are defined as every Nth image where N is an integer value that is greater than 1, defined as images obtained after the mobile device has moved more than a predefined or configured distance, or defined an image for which an overlap between the image and a last keyframe is less than a predefined or configured amount of overlap.

In one embodiment, determining the desired bitrate for compression of the image based on the one or more network conditions of the wireless network is in accordance with a bitrate adaptation scheme that dynamically adapts bitrate according to available network bandwidth.

In one embodiment, the method is repeated for a plurality of images.

Corresponding embodiments of a mobile device are also disclosed. In one embodiment, a mobile device for server-assisted localization and mapping is adapted to obtain an image of an environment in which the mobile device is located from an associated camera and determine a desired bitrate for compression of the image based on one or more network conditions of a wireless network over which the mobile device is to transmit the image to an associated server for server-assisted localization and mapping. The mobile device is further adapted to determine whether one or more criteria for transmission of the image at the desired bitrate are satisfied, the one or more criteria comprising a criterion that the image is to be transmitted at the desired bitrate if the desired bitrate is greater than a predefined or configured threshold bitrate for server-assisted localization and mapping. The mobile device is further adapted to, if the one or more criteria are not satisfied, perform a feature extraction procedure that detects one or more features in the image and provides feature information comprising information that describes the one or more features detected in the image and information that indicates locations of the one or more features within the image and transmit the feature information to the associated server for server-assisted localization and mapping. The mobile device is further adapted to, if any of the one or more criteria are satisfied, compress the image at the desired bitrate to provide a compressed image and transmit the compressed image to the associated server for server-assisted localization and mapping.

In one embodiment, a non-transitory computer-readable medium stores instructions executable by processing circuitry of a mobile device whereby mobile device is operable to obtain an image of an environment in which the mobile device is located from an associated camera, determine a desired bitrate for compression of the image based on one or more network conditions of a wireless network over which the mobile device is to transmit the image to an associated server for server-assisted localization and mapping, and determine) whether one or more criteria for transmission of the image at the desired bit are satisfied, the one or more criteria comprising a criterion that the image is to be transmitted at the desired bitrate if the desired bitrate is greater than a predefined or configured threshold bitrate for server-assisted localization and mapping. By execution of the instructions by the processing circuitry, the mobile device is further operable to, if the one or more criteria are not satisfied, perform a feature extraction procedure that detects one or more features in the image and provides feature information comprising information that describes the one or more features detected in the image and information that indicates locations of the one or more features within the image and transmit the feature information to the associated server for server-assisted localization and mapping. By execution of the instructions by the processing circuitry, the mobile device is further operable to, if any of the one or more criteria are satisfied, compress the image at the desired bitrate to provide a compressed image and transmit the compressed image to the associated server for server-assisted localization and mapping.

The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

Mobile Device: A mobile device is any type of wireless device that has access to (i.e., is served by) a wireless network (e.g., a cellular network). Some examples of a mobile device include, but are not limited to: a User Equipment device (UE) in a Third Generation Partnership Project (3GPP) network, a Machine Type Communication (MTC) device, and an Internet of Things (IoT) device. Such devices may be, or may be integrated into, another device, which may be another type of mobile device, such as, e.g., a mobile phone, smart phone, vehicle, virtual reality (VR) glasses, augmented reality (AR) glasses, robotic device, or the like, or integrated into any type of device for which localization is desired. The mobile device may be enabled to communicate voice and/or data via a wireless connection

It is to be appreciated that in the present disclosure, while reference is made to capturing, encoding, compressing, and/or transmitting “images”, these images can be frames of a video stream that are encoded and compressed via a video codec such as AVC/H.264, HEVC/H.265, or VVC/H.266. The video stream comprises the images at a certain rate or frames per second, and with a defined bitrate that is controlled by the video codec. Alternatively, the images can be individual images that are not part of a video stream, but are individually encoded and/or compressed to a predefined size, where the bitrate of the encoded and/or compressed images corresponds to a function of the size of the images and the rate at which the images are transmitted.

Localization and mapping, which is also known as Simultaneous Localization and Mapping (SLAM), refers to a computer-implemented procedure for constructing or updating a map an unknown environment while simultaneously keeping track of a mobile device's location within the unknown environment. Existing SLAM procedures are oftentimes implemented at the mobile device and are very energy demanding. As such, there is a need for systems and methods that reduce the energy demands of a SLAM procedure on the mobile device.

One way to reduce the energy demands of a SLAM procedure on a mobile device is to offload the SLAM procedure or at least some aspects of the SLAM procedure to an edge computing node or the “cloud”. Such offloading has been shown to greatly increase the battery lifetime of the mobile device even when considering the cost of streaming raw sensor data (e.g., raw images) to the edge computing node or the cloud in real-time. However, such streaming imposes a heavy demand on the wireless network. Therefore, systems and methods are disclosed herein for applying compression of the raw sensor data at the mobile device in order to reduce the demand on the wireless network. However, such compression may negatively impact the performance of the SLAM procedure. Thus, the systems and methods disclosed herein enable compression of raw sensor data (e.g., the raw images) at the mobile device prior to transmission over the wireless network in a manner that achieves at least a predefined or configured minimum acceptable performance for the SLAM procedure.

1 FIG. When evaluating the performance of the SLAM procedure as a function of the level of compression (i.e., the bitrate used for compression), the inventors found that one could find a minimum bitrate for which acceptable performance of the SLAM procedure was achieved. If the bitrate is lower than such value, performance SLAM procedure becomes significantly worse (e.g., ~5 centimeters (cm) to nearly 10 cm localization, or position, error for the evaluated scenario—see plot in). As described below, this minimum bitrate for which acceptable performance of the SLAM procedure is leveraged by the systems and methods disclosed herein to enable server-assisted SLAM using compressed image data in a manner that maintains the desired minimum acceptable performance of the SLAM procedure.

Visual SLAM procedures operate and create maps based on image features which can be extracted from images using state-of-the-art feature detectors and descriptors such as the Scale Invariant Feature Transform (SIFT) and Oriented Features from Accelerated Segment Test (FAST) and Rotated Binary Robust Independent Elementary Features (BRIEF) (ORB) feature detectors. SIFT and ORB provide lightweight descriptors of features detected in an image and can greatly reduce data requirements over full image transmission and storage.

State-of-the-art SLAM procedures that perform offline optimizations in order to improve the accuracy of the obtained map require full images (also called keyframes), where further feature extraction and pose (i.e., camera position and orientation) estimation given such features is performed. Even though a typical SLAM map is sparse, lightweight, and only used for localization purposes, applications such as robotics and Extended Reality (XR) require a denser map representation, which is typically performed by 3-Dimensional (3D) reconstruction of the keyframes which are part of the sparse SLAM map. Determining which images are keyframes in a sequence of images is a heuristic procedure where each SLAM algorithm defines its own, which can be based on a time period (e.g. every Nth frame or image is a keyframe), or if the device has travelled more than a predefined or configured distance (e.g., X meters), or when the overlap between the current image and the last identified keyframe is lower than a predefined or configured amount of overlap (e.g., Y %).

When performing server-assisted SLAM, the volumes of data to be transmitted over the network are very large. This brings the desire to compress this data. Further, bitrate adaptation procedures which determine the desired bitrate for an image stream given the available network bandwidth are known. However, such bitrate adaptation procedures may request a bitrate which degrades the performance of the server-assisted SLAM (e.g., degrades the localization performance) beyond acceptable values.

Systems and methods are disclosed herein for enabling server-assisted SLAM using compressed image data while maintaining at least a minimum acceptable performance for the SLAM procedure. In one embodiment, when the desired bitrate for image compression as determined by a bitrate adaptation procedure is below a predefined or configured minimum bitrate threshold that corresponds to a minimum acceptable SLAM performance, rather than transmitting a compressed image, the mobile device pre-processes the image to detect one or more image features from the image (e.g., one or more features to be used for the server-assisted SLAM) and provide information that describes the one or more detected image features and then transmits this information to the server. Conversely, when the desired bitrate for image compression as determined by the bitrate adaptation procedure is above the predefined or configured minimum bitrate threshold, the mobile device compresses the image at the desired bitrate and transmits the compressed image to the server. In this way, the desired bitrate is achieved while the SLAM performance is not compromised.

It should be noted that while the embodiments described herein refer to bitrate, any parameter related to an amount of compression or compression level can alternatively be used.

2 FIG. 2 FIG. 200 202 204 206 200 204 200 204 200 In this regard,illustrates a server-assisted SLAM procedure using compressed image data in which joint bitrate adaptation and feature extraction is provided such that the performance of the server-assisted SLAM procedure remains at least at an acceptable performance level, in accordance with one embodiment of the present disclosure. As illustrated, the procedure involves a mobile device, which includes a SLAM client, and an edge computing node, which includes a SLAM server. The mobile deviceis a wireless device that is enabled to transmit and receive data via an associated wireless network (e.g., a 3GPP cellular communications system), and the edge computing nodeis a computing node, which may be a physical or virtualized computing node. While not illustrated in, the mobile deviceand the edge computing node(e.g., via a wired or wireless connection) communicate via a wireless network such as, e.g., a 3GPP 5G System (5GS) or Evolved Packet System (EPS) or any similar wireless communication system. In one embodiment, the mobile deviceis a UE of a 3GPP 5GS, EPS, or similar wireless communication system.

200 202 200 208 200 200 200 2 FIG. As illustrated, the mobile device(e.g., the SLAM client) obtains an image of an environment in which the mobile deviceis located from an associated camera (step). The associated camera may be part of (i.e., integrated into) the mobile deviceor separate from the mobile device. If separate, the camera is communicatively coupled to the mobile devicevia a wireless or wireless interface (e.g., Bluetooth® interface or WiFi® interface). Note that, in one embodiment, images are obtained at a desired image capture rate for the server-assisted SLAM procedure (e.g., at a rate of 20 or 30 images per second). Thus, the procedure ofmay be repeated for each image in this stream of captured images.

200 202 200 206 204 210 202 200 The mobile device(e.g., the SLAM client) determines a desired bitrate for compression of the image based on one or network conditions of the wireless network over which the mobile deviceis to transmit the image to the SLAM serverat the edge computing node(step). As used herein, the “desired bitrate” is a bitrate for transmission over the wireless network determined by a bitrate adaptation procedure or function. Note that any suitable bitrate adaptation procedure may be used to determine the desired bitrate for transmission over the wireless network. In general, the bitrate adaptation procedure uses one or more network metrics (e.g., available bandwidth) to select a desired bitrate (e.g., from a predefined range of available bitrates). One non-limiting example of a bitrate adaptation procedure that can be used is the Self-Clocked Rate Adaptation for Multimedia procedure, which is sometimes referred to as “SCReAM”. In one embodiment, the one or more network conditions considered include available bandwidth in the wireless network. Note that the bitrate adaptation procedure may be performed by the SLAM clientor, e.g., a separate bitrate adaptation function of the mobile device.

202 200 212 1 FIG. The SLAM clientat the mobile devicedetermines whether one or more criteria for transmission of the image at the desired bitrate are satisfied (step). The one or more criteria include a criterion that the image is to be transmitted at the desired bitrate if the desired bitrate is greater than a predefined or configured threshold bitrate for server-assisted SLAM. This threshold bitrate is a bitrate that corresponds to a minimum acceptable performance level for the server-assisted SLAM procedure. In one embodiment, the threshold bitrate is predefined and is determined in advance via, e.g., simulations or experiments with real data captured by the mobile device in one or more real locations. As an example, for a particular SLAM procedure evaluated by the inventors, the inventors empirically found a minimum acceptable bitrate for which there was minimum degradation of that SLAM procedure in terms of localization performance (600 kilobits per second (kbps) bitrate for a 2 cm increased localization error—see). This type of experimentation to determine the bitrate threshold for an environment can be performed by applying different bitrates and executing the localization process on data collected by one or more devices in the given environment.

200 200 In one embodiment, the one or more criteria additionally or alternatively include a criterion that the image is to be transmitted at the desired bitrate if the image is a keyframe for the server-assisted SLAM procedure, even if the desired bitrate is less than the bitrate threshold. Determining which images are keyframes in a sequence of images is a heuristic procedure where each SLAM algorithm defines its own. In one embodiment, keyframes are based on a time period (e.g., every Nth image is a keyframe). In another embodiment, keyframes are based on distance travelled by the mobile devicesince the last (i.e., immediately preceding in time) keyframe (e.g., if the mobile devicehas move more than a predefined or configured distance (e.g., X meters) since the last keyframe, then the image is a keyframe). In another embodiment, keyframes are based on the amount of overlap with the last (i.e., immediately preceding in time) keyframe (e.g., if overlap between the image overlaps and the last keyframe is less than a predefined or configured amount of overlay (e.g., less than a Y % overlap), the image is a keyframe).

202 214 202 206 216 If the one or more criteria are not satisfied (e.g., if the desired bitrate is less than the bitrate threshold for acceptable SLAM performance and, optionally in some embodiments, if the image is not a keyframe), the SLAM clientperforms a feature extraction procedure that detects one or more features in the image (e.g., one or more features to be used by the server-assisted SLAM procedure) and provides feature information (step). This feature information is information about the features detected in the image such as, e.g., information (e.g., feature descriptors) that describes the one or more features detected in the image and information that indicates locations of the one or more detected features within the image (e.g., as pixel locations within the image). The SLAM clienttransmits the feature information, rather than the image or a compressed version thereof, to the SLAM server(step). Features are parts or patterns of an object in an image that are of interest for a particular purpose(s), which for the present disclosure is SLAM. For example, features may include edges of objects, lines, a curve, points, or the like. As a specific example, consider a square object within an image. The square as four corners and four edges, all of which may be detected as features of the object within the image.

206 In one embodiment, the feature information includes feature descriptors that describe the one or more features detected in the image, where these feature descriptors use the same format as used by the SLAM serverfor the server-assisted SLAM procedure. For example, in one embodiment, the feature descriptors are Binary Robust Independent Elementary Features (BRIEF) descriptors of the one or more detected features. The feature extraction procedure may be, for example, Features from Accelerated Segment Test (FAST), Oriented FAST and Rotated BRIEF (ORB), Scale-Invariant Feature Transform (SIFT), or the like. However, these feature extraction procedures are only examples. Any feature extraction procedure may be used. As an example, with ORB features used in ORB SLAM, a total of 500 features is detected per image, with a descriptor size of 32 bytes. This would amount to ~15 Kilobytes (KB) per image, which at 30 frames per second (fps), would require a total bandwidth of 450 kbps. The transmission of the feature information will provide a lower bandwidth utilization than performing a complete image compression, while SLAM algorithms can operate directly with features instead of full images.

200 206 Note that performing feature extraction and transmitting the resulting feature information for all images may not be desirable. Firstly, performing feature extraction for all images increases computational complexity and thus power consumption at the mobile device. Further, a typical SLAM map is sparse, lightweight, and only used for localization purposes, while applications such as robotics and XR require a denser map representation, which is typically performed by “3D reconstruction” algorithms on the “keyframes” which are part of the sparse SLAM map. The same rationale can be applied if semantic understanding of a scene is also required to be performed in conjunction with SLAM, which also operates on a full image. Thus, in certain cases it is desirable for the image itself, rather than the feature information, to be transmitted to the SLAM serverwhen possible.

202 218 206 220 If any of the one or more criteria are satisfied (e.g., if the desired bitrate is greater than the bitrate threshold for acceptable SLAM performance or, optionally in some embodiments, if the image is a keyframe), the SLAM clientcompresses the image at the desired bitrate to provide a compressed image (step) and transmits the compressed image to the SLAM server(step). Any suitable compression scheme and transmission protocol may be used. Some examples are H264 and Gstreamer.

208 220 222 204 206 216 220 224 The process of steps-may be repeated for multiple images (e.g., for a stream of images captured at a desired image capture rate for the server-assisted SLAM procedure) (step). At the edge computing node, the SLAM serverreceive the feature descriptors in stepor the image in stepand, based on this received information, performs SLAM (step).

3 FIG. 2 FIG. 300 300 302 304 1 304 2 306 1 306 2 304 1 304 2 304 304 306 1 306 2 306 306 308 1 308 4 310 1 310 4 308 1 308 4 310 1 310 4 304 308 1 308 4 308 308 310 1 310 4 310 310 304 308 310 304 308 200 1 200 5 200 illustrates one example of a systemin which embodiments of the present disclosure may be implemented. In the embodiments described herein, the systemincludes a cellular communications system (e.g., a 5G system (5GS) or Evolved Packet System (EPS)) including a RAN (e.g., a Next Generation RAN (NG-RAN) in the case of a 5GS or an Evolved Universal Terrestrial RAN (E-UTRAN) in the case of an EPS) and a core network(e.g., a 5G Core (5GC) in the case of a 5GS or an Evolved Packet Core (EPC) in the case of an EPS). In this example, the RAN includes base stations-and-, which in the 5GS include NR base stations (gNBs) and optionally next generation eNBs (ng-eNBs) (e.g., LTE RAN nodes connected to the 5GC) and in the EPS include eNBs, controlling corresponding (macro) cells-and-. The base stations-and-are generally referred to herein collectively as base stationsand individually as base station. Likewise, the (macro) cells-and-are generally referred to herein collectively as (macro) cellsand individually as (macro) cell. The RAN may also include a number of low power nodes-through-controlling corresponding small cells-through-. The low power nodes-through-can be small base stations (such as pico or femto base stations) or RRHs, or the like. Notably, while not illustrated, one or more of the small cells-through-may alternatively be provided by the base stations. The low power nodes-through-are generally referred to herein collectively as low power nodesand individually as low power node. Likewise, the small cells-through-are generally referred to herein collectively as small cellsand individually as small cell. The base stations(and optionally the low power nodes) are connected to the core network. The base stationsand the low power nodesprovide service to mobile devices-through-, each of which is an example of the mobile deviceof.

300 204 206 204 206 302 200 1 200 5 200 202 204 206 2 FIG. 2 FIG. The systemalso includes the edge computing nodeincluding the SLAM serverof, where the edge computing nodeand thus the SLAM serverare connected to the core networkand are able to communicate with the mobile devices-through-via the cellular communications system. The mobile device(including the SLAM client), the edge computing node, and the SLAM serveroperate as described above with respect to.

4 FIG. 204 204 404 406 408 404 404 204 206 406 404 is a schematic block diagram of an example embodiment of the edge computing node. As illustrated, the edge computing nodeincludes one or more processors(e.g., Central Processing Units (CPUs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), and/or the like), memory, and a network interface. The one or more processorsare also referred to herein as processing circuitry. The one or more processorsoperate to provide one or more functions of the edge computing node, and in particular the SLAM server, as described herein. In some embodiments, the function(s) are implemented in software that is stored, e.g., in the memoryand executed by the one or more processors.

5 FIG. 204 204 204 204 500 502 500 504 506 508 510 204 206 500 500 510 204 500 is a schematic block diagram that illustrates a virtualized embodiment of the edge computing nodeaccording to some embodiments of the present disclosure. Optional features are represented by dashed boxes. As used herein, a “virtualized” edge computing node is an implementation of the edge computing nodein which at least a portion of the functionality of the edge computing nodeis implemented as a virtual component(s) (e.g., via a virtual machine(s) executing on a physical processing node(s) in a network(s)). As illustrated, in this example, the edge computing nodeincludes one or more processing nodescoupled to or included as part of a network(s). Each processing nodeincludes one or more processors(e.g., CPUs, ASICs, FPGAs, and/or the like), memory, and a network interface. In this example, functionsof the edge computing node, and in particular the SLAM server, described herein are implemented at the one or more processing nodesor distributed across the two or more processing nodesin any desired manner. In some particular embodiments, some or all of the functionsof the edge computing nodedescribed herein are implemented as virtual components executed by one or more virtual machines implemented in a virtual environment(s) hosted by the processing node(s).

204 206 500 510 204 In some embodiments, a computer program including instructions which, when executed by at least one processor, causes the at least one processor to carry out the functionality of the edge computing node, or the SLAM server, or a node (e.g., a processing node) implementing one or more of the functionsof the edge computing nodein a virtual environment according to any of the embodiments described herein is provided. In some embodiments, a carrier comprising the aforementioned computer program product is provided. The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory).

6 FIG. 5 FIG. 204 204 600 600 204 206 500 600 500 500 is a schematic block diagram of the edge computing nodeaccording to some other embodiments of the present disclosure. The edge computing nodeincludes one or more modules, each of which is implemented in software. The module(s)provide the functionality of the edge computing node, and more specifically the SLAM server, described herein. This discussion is equally applicable to the processing nodeofwhere the modulesmay be implemented at one of the processing nodesor distributed across multiple processing nodes.

7 FIG. 7 FIG. 200 200 702 704 706 708 710 712 706 712 712 702 702 705 707 200 202 704 702 200 200 200 is a schematic block diagram of an example embodiment of the mobile device. As illustrated, the mobile deviceincludes one or more processors(e.g., CPUs, ASICs, FPGAs, and/or the like), memory, and one or more transceiverseach including one or more transmittersand one or more receiverscoupled to one or more antennas. The transceiver(s)includes radio-front end circuitry connected to the antenna(s)that is configured to condition signals communicated between the antenna(s)and the processor(s), as will be appreciated by on of ordinary skill in the art. The processorsandare also referred to herein as processing circuitry. The transceiversare also referred to herein as radio circuitry. In some embodiments, the functionality of the mobile device, and in particular that of the SLAM client, described above may be fully or partially implemented in software that is, e.g., stored in the memoryand executed by the processor(s). Note that the mobile devicemay include additional components not illustrated insuch as, e.g., one or more user interface components (e.g., an input/output interface including a display, buttons, a touch screen, a microphone, a speaker(s), and/or the like and/or any other components for allowing input of information into the mobile deviceand/or allowing output of information from the mobile device), a power supply (e.g., a battery and associated power circuitry), etc.

200 202 In some embodiments, a computer program including instructions which, when executed by at least one processor, causes the at least one processor to carry out the functionality of the mobile device, or the SLAM client, according to any of the embodiments described herein is provided. In some embodiments, a carrier comprising the aforementioned computer program product is provided. The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory).

Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processors (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), Random Access Memory (RAM), cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according one or more embodiments of the present disclosure.

While processes in the figures may show a particular order of operations performed by certain embodiments of the present disclosure, it should be understood that such order is exemplary (e.g., alternative embodiments may perform the operations in a different order, combine certain operations, overlap certain operations, etc.).

Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.